Introduction

In this notebook we will explore the extracted features from the WESAD dataset.

%reload_ext pretty_jupyter

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics

import sklearn.feature_selection as fs

import seaborn as sns
import plotly_express as px
import plotly.offline as pyo
import matplotlib.pyplot as plt

pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)
pyo.init_notebook_mode()

General Analysis

First, we import the dataset.

data = pd.read_csv('../data/03_primary/WESAD/combined_subjects.csv')
Data Preview
Unnamed: 0 net_acc_mean net_acc_std net_acc_min net_acc_max EDA_phasic_mean EDA_phasic_std EDA_phasic_min EDA_phasic_max EDA_smna_mean EDA_smna_std EDA_smna_min EDA_smna_max EDA_tonic_mean EDA_tonic_std EDA_tonic_min EDA_tonic_max BVP_mean BVP_std BVP_min BVP_max TEMP_mean TEMP_std TEMP_min TEMP_max ACC_x_mean ACC_x_std ACC_x_min ACC_x_max ACC_y_mean ACC_y_std ACC_y_min ACC_y_max ACC_z_mean ACC_z_std ACC_z_min ACC_z_max 0_mean 0_std 0_min 0_max BVP_peak_freq TEMP_slope subject label
0 0 1.331891 0.153556 1.014138 1.678399 2.247876 1.112076 0.367977 4.459367 1.592308 2.645333 3.096905e-08 17.418821 0.608263 1.212010 -1.213173 2.554750 -0.043934 112.391233 -392.28 554.77 35.816000 0.017436 35.77 35.87 0.024658 0.018284 -0.037843 0.087383 0.000017 0.000013 -0.000026 0.000060 0.000017 0.000013 -0.000026 0.000060 0.027558 0.013523 0.000000 0.087383 0.080556 -0.000102 2 1
1 1 1.218994 0.090108 1.014138 1.485800 1.781323 1.203991 0.232625 4.459367 1.347750 2.666659 3.096905e-08 17.418821 0.731985 1.171627 -1.213173 2.477276 -1.189267 120.431399 -392.28 554.77 35.796111 0.029522 35.75 35.87 0.020313 0.019242 -0.037843 0.087383 0.000014 0.000013 -0.000026 0.000060 0.000014 0.000013 -0.000026 0.000060 0.023420 0.015310 0.000000 0.087383 0.144444 -0.000424 2 1
2 2 1.143312 0.110987 0.948835 1.485800 1.173169 1.285422 0.006950 4.459367 0.752335 1.958546 3.096905e-08 17.418821 1.110242 1.112268 -1.213173 2.037179 0.280427 87.571000 -357.53 371.12 35.763056 0.044673 35.68 35.87 0.016618 0.015316 -0.021330 0.071558 0.000011 0.000011 -0.000015 0.000049 0.000011 0.000011 -0.000015 0.000049 0.018759 0.012604 0.000000 0.071558 0.102778 -0.000814 2 1
3 3 1.020669 0.135308 0.811090 1.239944 0.311656 0.278650 0.006950 1.303071 0.198576 0.413802 3.309990e-08 2.788862 1.598995 0.350355 0.959752 2.037179 0.055833 68.797466 -345.19 359.57 35.725000 0.033491 35.66 35.81 0.022681 0.012560 -0.006881 0.054356 0.000016 0.000009 -0.000005 0.000037 0.000016 0.000009 -0.000005 0.000037 0.022888 0.012180 0.000688 0.054356 0.108333 -0.000524 2 1
4 4 0.887458 0.116048 0.727406 1.125306 0.163826 0.110277 0.006950 0.369298 0.118080 0.237575 2.787285e-08 1.300810 1.342085 0.405980 0.945946 2.037179 0.096681 43.606312 -289.26 209.89 35.701333 0.022420 35.66 35.75 0.028105 0.010415 0.002752 0.054356 0.000019 0.000007 0.000002 0.000037 0.000019 0.000007 0.000002 0.000037 0.028105 0.010415 0.002752 0.054356 0.147222 -0.000165 2 1
5 5 0.776920 0.071154 0.681346 0.956575 0.155098 0.115413 0.002306 0.369298 0.113253 0.233061 2.787285e-08 1.289171 1.015119 0.158530 0.817326 1.513996 -0.642795 52.948702 -289.26 209.89 35.705056 0.023058 35.66 35.75 0.034358 0.004849 0.002752 0.054356 0.000024 0.000003 0.000002 0.000037 0.000024 0.000003 0.000002 0.000037 0.034358 0.004849 0.002752 0.054356 0.138889 0.000261 2 1
6 6 0.705557 0.055554 0.608254 0.819336 0.080122 0.092646 0.002306 0.319375 0.048063 0.151028 2.787285e-08 1.105898 0.873283 0.105136 0.656496 1.013622 -0.037437 41.045187 -199.01 194.12 35.721444 0.028090 35.66 35.77 0.031188 0.004681 0.013761 0.039907 0.000021 0.000003 0.000009 0.000027 0.000021 0.000003 0.000009 0.000027 0.031188 0.004681 0.013761 0.039907 0.138889 0.000460 2 1
7 7 0.639991 0.054349 0.543110 0.725169 0.022266 0.034928 0.000015 0.132781 0.016674 0.090613 5.174644e-08 0.997037 0.732013 0.147837 0.460235 0.999065 -0.083809 35.416182 -197.37 194.12 35.753111 0.029950 35.71 35.81 0.029377 0.004256 0.013761 0.038531 0.000020 0.000003 0.000009 0.000027 0.000020 0.000003 0.000009 0.000027 0.029377 0.004256 0.013761 0.038531 0.152778 0.000516 2 1
8 8 0.580220 0.054845 0.486494 0.685270 0.024059 0.037475 0.000015 0.167825 0.025170 0.089431 3.297693e-08 0.601262 0.548576 0.180334 0.146098 0.816318 0.548538 57.092149 -367.11 363.29 35.783667 0.033894 35.73 35.84 0.027603 0.007144 -0.002752 0.066053 0.000019 0.000005 -0.000002 0.000045 0.000019 0.000005 -0.000002 0.000045 0.027618 0.007088 0.000000 0.066053 0.152778 0.000593 2 1
9 9 0.532770 0.036903 0.474375 0.607551 0.165363 0.216325 0.000015 0.669836 0.152681 0.475520 3.284132e-08 3.622407 0.263263 0.287734 -0.202700 0.653034 -0.310028 96.934155 -670.20 363.29 35.814722 0.028076 35.75 35.87 0.028278 0.010877 -0.030962 0.074998 0.000019 0.000007 -0.000021 0.000052 0.000019 0.000007 -0.000021 0.000052 0.028672 0.009792 0.000000 0.074998 0.122222 0.000447 2 1

We can observe that the all the data is numeric and there are no missing values. We will remove the first column as it is just a clone of the index.

data = data.drop([data.columns[0]], axis=1)
Modified Data Preview
net_acc_mean net_acc_std net_acc_min net_acc_max EDA_phasic_mean EDA_phasic_std EDA_phasic_min EDA_phasic_max EDA_smna_mean EDA_smna_std EDA_smna_min EDA_smna_max EDA_tonic_mean EDA_tonic_std EDA_tonic_min EDA_tonic_max BVP_mean BVP_std BVP_min BVP_max TEMP_mean TEMP_std TEMP_min TEMP_max ACC_x_mean ACC_x_std ACC_x_min ACC_x_max ACC_y_mean ACC_y_std ACC_y_min ACC_y_max ACC_z_mean ACC_z_std ACC_z_min ACC_z_max 0_mean 0_std 0_min 0_max BVP_peak_freq TEMP_slope subject label
0 1.331891 0.153556 1.014138 1.678399 2.247876 1.112076 0.367977 4.459367 1.592308 2.645333 3.096905e-08 17.418821 0.608263 1.212010 -1.213173 2.554750 -0.043934 112.391233 -392.28 554.77 35.816000 0.017436 35.77 35.87 0.024658 0.018284 -0.037843 0.087383 0.000017 0.000013 -0.000026 0.000060 0.000017 0.000013 -0.000026 0.000060 0.027558 0.013523 0.000000 0.087383 0.080556 -0.000102 2 1
1 1.218994 0.090108 1.014138 1.485800 1.781323 1.203991 0.232625 4.459367 1.347750 2.666659 3.096905e-08 17.418821 0.731985 1.171627 -1.213173 2.477276 -1.189267 120.431399 -392.28 554.77 35.796111 0.029522 35.75 35.87 0.020313 0.019242 -0.037843 0.087383 0.000014 0.000013 -0.000026 0.000060 0.000014 0.000013 -0.000026 0.000060 0.023420 0.015310 0.000000 0.087383 0.144444 -0.000424 2 1
2 1.143312 0.110987 0.948835 1.485800 1.173169 1.285422 0.006950 4.459367 0.752335 1.958546 3.096905e-08 17.418821 1.110242 1.112268 -1.213173 2.037179 0.280427 87.571000 -357.53 371.12 35.763056 0.044673 35.68 35.87 0.016618 0.015316 -0.021330 0.071558 0.000011 0.000011 -0.000015 0.000049 0.000011 0.000011 -0.000015 0.000049 0.018759 0.012604 0.000000 0.071558 0.102778 -0.000814 2 1
3 1.020669 0.135308 0.811090 1.239944 0.311656 0.278650 0.006950 1.303071 0.198576 0.413802 3.309990e-08 2.788862 1.598995 0.350355 0.959752 2.037179 0.055833 68.797466 -345.19 359.57 35.725000 0.033491 35.66 35.81 0.022681 0.012560 -0.006881 0.054356 0.000016 0.000009 -0.000005 0.000037 0.000016 0.000009 -0.000005 0.000037 0.022888 0.012180 0.000688 0.054356 0.108333 -0.000524 2 1
4 0.887458 0.116048 0.727406 1.125306 0.163826 0.110277 0.006950 0.369298 0.118080 0.237575 2.787285e-08 1.300810 1.342085 0.405980 0.945946 2.037179 0.096681 43.606312 -289.26 209.89 35.701333 0.022420 35.66 35.75 0.028105 0.010415 0.002752 0.054356 0.000019 0.000007 0.000002 0.000037 0.000019 0.000007 0.000002 0.000037 0.028105 0.010415 0.002752 0.054356 0.147222 -0.000165 2 1
5 0.776920 0.071154 0.681346 0.956575 0.155098 0.115413 0.002306 0.369298 0.113253 0.233061 2.787285e-08 1.289171 1.015119 0.158530 0.817326 1.513996 -0.642795 52.948702 -289.26 209.89 35.705056 0.023058 35.66 35.75 0.034358 0.004849 0.002752 0.054356 0.000024 0.000003 0.000002 0.000037 0.000024 0.000003 0.000002 0.000037 0.034358 0.004849 0.002752 0.054356 0.138889 0.000261 2 1
6 0.705557 0.055554 0.608254 0.819336 0.080122 0.092646 0.002306 0.319375 0.048063 0.151028 2.787285e-08 1.105898 0.873283 0.105136 0.656496 1.013622 -0.037437 41.045187 -199.01 194.12 35.721444 0.028090 35.66 35.77 0.031188 0.004681 0.013761 0.039907 0.000021 0.000003 0.000009 0.000027 0.000021 0.000003 0.000009 0.000027 0.031188 0.004681 0.013761 0.039907 0.138889 0.000460 2 1
7 0.639991 0.054349 0.543110 0.725169 0.022266 0.034928 0.000015 0.132781 0.016674 0.090613 5.174644e-08 0.997037 0.732013 0.147837 0.460235 0.999065 -0.083809 35.416182 -197.37 194.12 35.753111 0.029950 35.71 35.81 0.029377 0.004256 0.013761 0.038531 0.000020 0.000003 0.000009 0.000027 0.000020 0.000003 0.000009 0.000027 0.029377 0.004256 0.013761 0.038531 0.152778 0.000516 2 1
8 0.580220 0.054845 0.486494 0.685270 0.024059 0.037475 0.000015 0.167825 0.025170 0.089431 3.297693e-08 0.601262 0.548576 0.180334 0.146098 0.816318 0.548538 57.092149 -367.11 363.29 35.783667 0.033894 35.73 35.84 0.027603 0.007144 -0.002752 0.066053 0.000019 0.000005 -0.000002 0.000045 0.000019 0.000005 -0.000002 0.000045 0.027618 0.007088 0.000000 0.066053 0.152778 0.000593 2 1
9 0.532770 0.036903 0.474375 0.607551 0.165363 0.216325 0.000015 0.669836 0.152681 0.475520 3.284132e-08 3.622407 0.263263 0.287734 -0.202700 0.653034 -0.310028 96.934155 -670.20 363.29 35.814722 0.028076 35.75 35.87 0.028278 0.010877 -0.030962 0.074998 0.000019 0.000007 -0.000021 0.000052 0.000019 0.000007 -0.000021 0.000052 0.028672 0.009792 0.000000 0.074998 0.122222 0.000447 2 1

Dataset Column Overview

data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2091 entries, 0 to 2090
Data columns (total 44 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   net_acc_mean     2091 non-null   float64
 1   net_acc_std      2091 non-null   float64
 2   net_acc_min      2091 non-null   float64
 3   net_acc_max      2091 non-null   float64
 4   EDA_phasic_mean  2091 non-null   float64
 5   EDA_phasic_std   2091 non-null   float64
 6   EDA_phasic_min   2091 non-null   float64
 7   EDA_phasic_max   2091 non-null   float64
 8   EDA_smna_mean    2091 non-null   float64
 9   EDA_smna_std     2091 non-null   float64
 10  EDA_smna_min     2091 non-null   float64
 11  EDA_smna_max     2091 non-null   float64
 12  EDA_tonic_mean   2091 non-null   float64
 13  EDA_tonic_std    2091 non-null   float64
 14  EDA_tonic_min    2091 non-null   float64
 15  EDA_tonic_max    2091 non-null   float64
 16  BVP_mean         2091 non-null   float64
 17  BVP_std          2091 non-null   float64
 18  BVP_min          2091 non-null   float64
 19  BVP_max          2091 non-null   float64
 20  TEMP_mean        2091 non-null   float64
 21  TEMP_std         2091 non-null   float64
 22  TEMP_min         2091 non-null   float64
 23  TEMP_max         2091 non-null   float64
 24  ACC_x_mean       2091 non-null   float64
 25  ACC_x_std        2091 non-null   float64
 26  ACC_x_min        2091 non-null   float64
 27  ACC_x_max        2091 non-null   float64
 28  ACC_y_mean       2091 non-null   float64
 29  ACC_y_std        2091 non-null   float64
 30  ACC_y_min        2091 non-null   float64
 31  ACC_y_max        2091 non-null   float64
 32  ACC_z_mean       2091 non-null   float64
 33  ACC_z_std        2091 non-null   float64
 34  ACC_z_min        2091 non-null   float64
 35  ACC_z_max        2091 non-null   float64
 36  0_mean           2091 non-null   float64
 37  0_std            2091 non-null   float64
 38  0_min            2091 non-null   float64
 39  0_max            2091 non-null   float64
 40  BVP_peak_freq    2091 non-null   float64
 41  TEMP_slope       2091 non-null   float64
 42  subject          2091 non-null   int64  
 43  label            2091 non-null   int64  
dtypes: float64(42), int64(2)
memory usage: 718.9 KB

Dataset Shape

(2091, 44)

Descriptive Statistics

Now we will explore the data. We will start by looking at the distribution of the features.

net_acc_mean net_acc_std net_acc_min net_acc_max EDA_phasic_mean EDA_phasic_std EDA_phasic_min EDA_phasic_max EDA_smna_mean EDA_smna_std EDA_smna_min EDA_smna_max EDA_tonic_mean EDA_tonic_std EDA_tonic_min EDA_tonic_max BVP_mean BVP_std BVP_min BVP_max TEMP_mean TEMP_std TEMP_min TEMP_max ACC_x_mean ACC_x_std ACC_x_min ACC_x_max ACC_y_mean ACC_y_std ACC_y_min ACC_y_max ACC_z_mean ACC_z_std ACC_z_min ACC_z_max 0_mean 0_std 0_min 0_max BVP_peak_freq TEMP_slope subject label
count 2091.000000 2091.000000 2091.000000 2091.000000 2.091000e+03 2.091000e+03 2.091000e+03 2.091000e+03 2.091000e+03 2.091000e+03 2.091000e+03 2.091000e+03 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2.091000e+03 2091.000000 2091.000000 2091.000000 2.091000e+03 2.091000e+03 2.091000e+03 2091.000000 2.091000e+03 2.091000e+03 2.091000e+03 2091.000000 2.091000e+03 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000 2091.000000
mean 1.966550 0.053456 1.855260 2.089283 1.700688e-01 1.118879e-01 3.403359e-02 4.413895e-01 1.300774e-01 2.672041e-01 6.123098e-08 1.826972e+00 -0.041869 0.113459 -0.255890 0.108671 -0.000153 49.919955 -270.200014 240.117805 33.018114 0.026576 32.967303 33.068604 0.011463 4.462103e-03 -0.007762 0.030142 0.000008 3.070164e-06 -5.340943e-06 2.073911e-05 0.000008 3.070164e-06 -5.340943e-06 2.073911e-05 0.029561 4.187458e-03 0.014872 0.048907 0.125485 -0.000015 9.404113 1.140603
std 2.657218 0.091732 2.496197 2.789401 5.482836e-01 4.572540e-01 1.063828e-01 1.489958e+00 4.227190e-01 7.230139e-01 4.659428e-08 5.546362e+00 1.226109 0.438957 1.669225 1.201522 0.573221 40.131618 238.855851 209.107222 1.470879 0.020533 1.467757 1.475477 0.028678 4.229046e-03 0.034671 0.032306 0.000020 2.909809e-06 2.385519e-05 2.222845e-05 0.000020 2.909809e-06 2.385519e-05 2.222845e-05 0.009287 3.689155e-03 0.013045 0.018166 0.039913 0.000565 4.706482 0.661542
min 0.091182 0.000742 0.074363 0.100672 1.135074e-07 1.525014e-08 6.445254e-08 1.709373e-07 8.388991e-08 1.827182e-08 3.479847e-09 1.532876e-07 -10.033692 0.000257 -25.222599 -2.216655 -5.428135 2.834831 -1617.860000 7.270000 29.381111 0.007700 29.330000 29.430000 -0.044579 5.759282e-16 -0.088071 -0.040595 -0.000031 6.403569e-19 -6.059740e-05 -2.793162e-05 -0.000031 6.403569e-19 -6.059740e-05 -2.793162e-05 0.000555 8.326673e-16 0.000000 0.004128 0.025000 -0.003220 2.000000 0.000000
25% 0.307707 0.004405 0.292997 0.321858 4.294791e-03 5.038863e-03 8.542278e-06 2.139904e-02 3.276647e-03 1.583230e-02 2.948547e-08 1.359000e-01 -0.795608 0.010061 -0.889842 -0.753547 -0.148356 23.048587 -352.120000 94.575000 32.274222 0.014900 32.230000 32.310000 -0.020900 9.221599e-04 -0.035779 0.000688 -0.000014 6.344950e-07 -2.461769e-05 4.734172e-07 -0.000014 6.344950e-07 -2.461769e-05 4.734172e-07 0.023266 9.206196e-04 0.002064 0.037843 0.097222 -0.000290 5.000000 1.000000
50% 0.846770 0.017041 0.772491 0.938149 2.018820e-02 2.109368e-02 3.296700e-04 8.417205e-02 1.534513e-02 5.711463e-02 4.831052e-08 5.035151e-01 -0.498252 0.034663 -0.525585 -0.440061 -0.003823 38.391360 -197.370000 189.240000 33.166889 0.019317 33.130000 33.230000 0.024675 3.285789e-03 0.000688 0.040595 0.000017 2.260798e-06 4.734172e-07 2.793162e-05 0.000017 2.260798e-06 4.734172e-07 2.793162e-05 0.030536 3.228065e-03 0.012385 0.048164 0.127778 -0.000053 9.000000 1.000000
75% 2.665476 0.063217 2.516406 2.884861 1.618663e-01 9.929476e-02 1.428761e-02 4.578447e-01 1.231743e-01 2.893434e-01 7.709707e-08 1.908303e+00 0.735160 0.096417 0.565363 0.989545 0.147601 64.203423 -100.530000 323.660000 34.011000 0.029580 33.950000 34.070000 0.036200 6.795670e-03 0.021330 0.051948 0.000025 4.675783e-06 1.467593e-05 3.574300e-05 0.000025 4.675783e-06 1.467593e-05 3.574300e-05 0.037319 6.645341e-03 0.025458 0.057796 0.150000 0.000171 14.000000 2.000000
max 15.632220 1.130964 14.720361 15.931444 1.197433e+01 1.044126e+01 1.838081e+00 2.963154e+01 9.223967e+00 1.419266e+01 2.929251e-07 1.172344e+02 3.028557 9.991237 2.890934 3.291220 4.628719 320.678627 -9.280000 1789.000000 35.933111 0.193635 35.910000 35.970000 0.043367 2.607680e-02 0.043347 0.087383 0.000030 1.794223e-05 2.982528e-05 6.012398e-05 0.000030 1.794223e-05 2.982528e-05 6.012398e-05 0.044579 1.874508e-02 0.043347 0.088071 0.319444 0.003682 17.000000 2.000000

Feature selection

After loading and observing the dataset, it's time to find best features.

# def get_best_features()
kBest = fs.SelectKBest(fs.f_classif, k=5)
res = kBest.fit_transform(data.drop(columns=['label']), data['label'])
filter = kBest.get_support()
df = pd.DataFrame(res, columns = data.columns[:-1][filter])
df = df.join(data['label'])
print(f"""Top features:\n{"  ".join(data.columns[:-1][filter])}""")
Top features:
net_acc_std  net_acc_max  EDA_tonic_mean  EDA_tonic_min  EDA_tonic_max
cdf = pd.concat([df.drop("label", axis=1), pd.get_dummies(df["label"])], axis=1)
cdf.rename(columns={0: "amusement", 1: "baseline", 2: "stress"}, inplace=True)

corr = cdf.corr()
fig = px.imshow(corr[["amusement", "baseline", "stress"]], text_auto=True)
fig = fig.update_layout(width=500, height=800)
fig.show()

Data analysis by subject

from plotly.subplots import make_subplots
import plotly.graph_objects as go
def plot_distribution(feature, nbr_cols=4):
    subjects = [2, 3, 4, 5, 6 ,7 ,8, 9, 10, 11, 13, 14, 15, 16, 17]
    titles = [f'Subject {x}' for x in subjects]
    plot = make_subplots(rows=len(subjects) // nbr_cols+1, cols=nbr_cols, subplot_titles=titles)

    row_n = 1
    col_n = 1
    for sub in subjects:
        csv = pd.read_csv(f'../data/03_primary/WESAD/subject_feats/S{sub}_feats_4.csv')
        plot.add_trace(go.Bar(y=csv[feature]), row_n, col_n, )
        col_n += 1
        if col_n > nbr_cols:
            col_n = 1
            row_n += 1

    plot.update_layout(height=1000)
    plot.show()

net_acc_std

plot_distribution('net_acc_std')

net_acc_max

plot_distribution('net_acc_max')

EDA_tonic_mean

plot_distribution('EDA_tonic_mean')

EDA_tonic_min

plot_distribution('EDA_tonic_min')

EDA_tonic_max

plot_distribution('EDA_tonic_max')

The effect of the stress level

def get_label(label):
    frame = df.loc[df.label==label]
    frame.index =  range(0, frame.shape[0])
    frame.index = pd.to_datetime(frame.index, unit='s')
    return frame
amusement = get_label(0)
baseline = get_label(1)
stress = get_label(2)
df.label = df.label.replace(0, 'amusement')
df.label = df.label.replace(1, 'baseline')
df.label = df.label.replace(2, 'stress')
df
net_acc_std net_acc_max EDA_tonic_mean EDA_tonic_min EDA_tonic_max label
0 0.153556 1.678399 0.608263 -1.213173 2.554750 baseline
1 0.090108 1.485800 0.731985 -1.213173 2.477276 baseline
2 0.110987 1.485800 1.110242 -1.213173 2.037179 baseline
3 0.135308 1.239944 1.598995 0.959752 2.037179 baseline
4 0.116048 1.125306 1.342085 0.945946 2.037179 baseline
... ... ... ... ... ... ...
2086 0.003381 1.011533 -0.307217 -0.320846 -0.286634 amusement
2087 0.002093 1.011533 -0.301157 -0.315003 -0.286634 amusement
2088 0.002072 1.011132 -0.305016 -0.310237 -0.296301 amusement
2089 0.001978 1.010419 -0.306060 -0.310788 -0.299483 amusement
2090 0.001819 1.010117 -0.311347 -0.330077 -0.306007 amusement

2091 rows × 6 columns

net_acc_std

fig = px.line(df, y='net_acc_std', color='label')
fig.show()

net_acc_max

fig = px.line(df, y='net_acc_max', color='label')
fig.show()

EDA_tonic_mean

fig = px.line(df, y='EDA_tonic_mean', color='label')
fig.show()

EDA_tonic_min

fig = px.line(df, y='EDA_tonic_min', color='label')
fig.show()

EDA_tonic_max

fig = px.line(df, y='EDA_tonic_max', color='label')
fig.show()